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Automatized medical chatbots are conversationally built with technology in mind with having the potential to reduce efforts to healthcare costs and improve access to medical services and knowledge. We built a diagnosis bot that engages patients in the conversation for their medical query and problems to provides an individualized diagnosis based on their diagnosed manifestation and profile. Our chatbot system is qualified to identify symptoms from user inputs with a standard precision of 65%. Using these extracted diagnosed symptoms correct symptoms were identified with a recall of 65% and a precision of 71%. Finally, the chatbot returned the expected diagnosis for further more operations. This determines that a medical chatbot can provide a somewhat accurate diagnosis to patients with simple symptom analysis and a conversational approach, this suggests that an effective spoken language medical bot could be viable. Moreover, the relative effectiveness of this bot indicates that more proceeds automated medical products may flourish to serve a bigger role in healthcare.
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Srivastava et al. (Sat,) studied this question.
synapsesocial.com/papers/6a0ef120aa1655e5fb230a67 — DOI: https://doi.org/10.1109/parc49193.2020.236624
Prakhar Srivastava
BML Munjal University
Nishant Singh
University of Zurich
GLA University
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